"""simple docstring"""


import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:






	class   __UpperCAmelCase		:

					@staticmethod
					def 						_a      ( *_lowerCamelCase      ,					**_lowerCamelCase ):
											pass






@is_pipeline_test
@require_vision
class   __UpperCAmelCase		(      unittest.TestCase      ):

				@require_torch
				def 						_a      ( self ):
										lowerCamelCase__   =pipeline(
										    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,					)
										lowerCamelCase__   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
										lowerCamelCase__   =image_classifier(_lowerCamelCase      ,					candidate_labels=["a", "b", "c"] )

										# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
										# python and torch versions.
										self.assertIn(
										    nested_simplify(_lowerCamelCase )      ,					[
										        [{"score": 0.3_3_3, "label": "a"}, {"score": 0.3_3_3, "label": "b"}, {"score": 0.3_3_3, "label": "c"}],
										        [{"score": 0.3_3_3, "label": "a"}, {"score": 0.3_3_3, "label": "c"}, {"score": 0.3_3_3, "label": "b"}],
										    ]      ,					)

										lowerCamelCase__   =image_classifier([image] * 5      ,					candidate_labels=["A", "B", "C"]      ,					batch_size=2 )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										    ]      ,					)

				@require_tf
				def 						_a      ( self ):
										lowerCamelCase__   =pipeline(
										    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,					framework="tf" )
										lowerCamelCase__   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
										lowerCamelCase__   =image_classifier(_lowerCamelCase      ,					candidate_labels=["a", "b", "c"] )

										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[{"score": 0.3_3_3, "label": "a"}, {"score": 0.3_3_3, "label": "b"}, {"score": 0.3_3_3, "label": "c"}]      ,					)

										lowerCamelCase__   =image_classifier([image] * 5      ,					candidate_labels=["A", "B", "C"]      ,					batch_size=2 )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										        [
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										            {"score": 0.3_3_3, "label": ANY(_lowerCamelCase )},
										        ],
										    ]      ,					)

				@slow
				@require_torch
				def 						_a      ( self ):
										lowerCamelCase__   =pipeline(
										    task="zero-shot-image-classification"      ,					model="openai/clip-vit-base-patch32"      ,					)
										# This is an image of 2 cats with remotes and no planes
										lowerCamelCase__   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
										lowerCamelCase__   =image_classifier(_lowerCamelCase      ,					candidate_labels=["cat", "plane", "remote"] )

										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        {"score": 0.5_1_1, "label": "remote"},
										        {"score": 0.4_8_5, "label": "cat"},
										        {"score": 0.0_0_4, "label": "plane"},
										    ]      ,					)

										lowerCamelCase__   =image_classifier([image] * 5      ,					candidate_labels=["cat", "plane", "remote"]      ,					batch_size=2 )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        [
										            {"score": 0.5_1_1, "label": "remote"},
										            {"score": 0.4_8_5, "label": "cat"},
										            {"score": 0.0_0_4, "label": "plane"},
										        ],
										    ]
										    * 5      ,					)






				@slow
				@require_tf
				def 						_a      ( self ):
										lowerCamelCase__   =pipeline(
										    task="zero-shot-image-classification"      ,					model="openai/clip-vit-base-patch32"      ,					framework="tf" )
										# This is an image of 2 cats with remotes and no planes
										lowerCamelCase__   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
										lowerCamelCase__   =image_classifier(_lowerCamelCase      ,					candidate_labels=["cat", "plane", "remote"] )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        {"score": 0.5_1_1, "label": "remote"},
										        {"score": 0.4_8_5, "label": "cat"},
										        {"score": 0.0_0_4, "label": "plane"},
										    ]      ,					)

										lowerCamelCase__   =image_classifier([image] * 5      ,					candidate_labels=["cat", "plane", "remote"]      ,					batch_size=2 )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        [
										            {"score": 0.5_1_1, "label": "remote"},
										            {"score": 0.4_8_5, "label": "cat"},
										            {"score": 0.0_0_4, "label": "plane"},
										        ],
										    ]
										    * 5      ,					)



"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						def is_in_circle(__lowerCAmelCase  ,       __lowerCAmelCase							) -> bool:
												lowerCamelCase__   =sqrt((x**2) + (y**2)							)
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						lowerCamelCase__   =mean(
						    int(is_in_circle(uniform(-1.0  ,       1.0							)  ,       uniform(-1.0  ,       1.0							)							)							)
						    for _ in range(__lowerCAmelCase							)							)
						# The ratio of the area for circle to square is pi/4.
						lowerCamelCase__   =proportion * 4
						print(F'''The estimated value of pi is {pi_estimate}'''							)
						print(F'''The numpy value of pi is {pi}'''							)
						print(F'''The total error is {abs(pi - pi_estimate							)}'''							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 0.0  ,       __lowerCAmelCase = 1.0  ,       )				->   float:




						'''simple docstring'''



						return mean(
						    function_to_integrate(uniform(__lowerCAmelCase  ,       __lowerCAmelCase							)							) for _ in range(__lowerCAmelCase							)							) * (max_value - min_value)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase = 0.0  ,       __lowerCAmelCase = 1.0							)				->   None:




						'''simple docstring'''



						def identity_function(__lowerCAmelCase							) -> float:
												return x

						lowerCamelCase__   =area_under_curve_estimator(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =(max_value * max_value - min_value * min_value) / 2

						print("******************"							)
						print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}'''							)
						print(F'''Estimated value is {estimated_value}'''							)
						print(F'''Expected value is {expected_value}'''							)
						print(F'''Total error is {abs(estimated_value - expected_value							)}'''							)
						print("******************"							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						def function_to_integrate(__lowerCAmelCase							) -> float:
												return sqrt(4.0 - x * x							)

						lowerCamelCase__   =area_under_curve_estimator(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       0.0  ,       2.0							)

						print("******************"							)
						print("Estimating pi using area_under_curve_estimator"							)
						print(F'''Estimated value is {estimated_value}'''							)
						print(F'''Expected value is {pi}'''							)
						print(F'''Total error is {abs(estimated_value - pi							)}'''							)
						print("******************"							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


